REM Sleep Behavior Disorder (RBD) is a significant predictor of neurodegenerative diseases such as Parkinson’s Disease, but its diagnosis currently requires resource-intensive polysomnography (PSG) studies, limiting widespread screening. This paper presents RBDGuard, a novel deep learning framework that enables RBD detection using single-lead electrocardiogram (ECG) data by leveraging both unlabeled and labeled datasets through unsupervised pre-training with supervised fine-tuning. This approach capitalizes on the increasing accessibility of ECG monitoring through wearable devices to offer an easy and inexpensive early diagnostic tool for sleep disorders. RBDGuard’s architecture employs bidirectional LSTM layers for unsupervised pre-training followed by dense layers with dropout for supervised classification. RBDGuard utilized 377,622 unlabeled 30 s ECG segments from 9,174 subjects and 39,316 labeled 30 s ECG segments from 37 subjects (22 RBD, 15 control) across three public databases. Compared to the exclusively supervised model, RBDGuard achieved significant improvements in accuracy (from 80 to 97%) and reduction in misclassification (a 75% decrease in false positives and 95% decrease in false negatives). RBDGuard maintained accuracies of 97–98% across different pre-training datasets, outperforming the supervised-only approach and indicating adaptability to different clinical settings. These promising results establish RBDGuard as an accurate, efficient tool for widespread early detection of RBD and neurodegenerative diseases.